def test_skewness_ones():
nbins = 256
period = 10.0 # ms
cc_amps = np.ones(nbins)
# gamma should be a very small, negative number in this case
gamma = skewness(cc_amps, period=period)
assert -1.0e-10 < gamma < 0
def test_skewness_single():
nbins = 256
period = 10.0
cc_amps = np.zeros(nbins)
cc_amps[nbins // 2] = 50
gamma = skewness(cc_amps, period=period)
assert gamma == 0 # a completely symmetric deconvolution means gamma should be identically 0
def test_skewness_cluster_symmetric():
nbins = 256
period = 10.0
cc_amps = np.zeros(nbins)
cc_amps[124:133] += 10
cc_amps[128] = 50
# gamma should be a number very close to zero
gamma = skewness(cc_amps, period=period)
assert abs(gamma) < 1.0e-8
def test_skewness_cluster_left_skew():
nbins = 256
period = 10.0
cc_amps = np.zeros(nbins)
# dominate with one component, but have a significant number of non-zero components to the left
cc_amps[124:133] += 10
cc_amps[132] = 200
# gamma should be a negative number less than -1
gamma = skewness(cc_amps, period=period)
assert gamma < -1
def test_skewness_cluster_right_skew():
nbins = 256
period = 10.0
cc_amps = np.zeros(nbins)
# dominate with one component, but have a significant number of non-zero components to the right
cc_amps[124:133] += 10
cc_amps[125] = 200
# gamma should be a positive number greater than 1
gamma = skewness(cc_amps, period=period)
assert gamma > 1